SS-IPLE: Semantic Segmentation of Electric Power Corridor Scene and Individual Power Line Extraction From UAV-Based Lidar Point Cloud

Key objects’ semantic segmentation and power line extraction from the electric power corridor point cloud are critical steps in power line inspection. However, the massive amount of point cloud data and missing power line points pose a challenge to the object extraction. To complete the extraction of power lines and other essential objects, a method called SS-IPLE is proposed, which is based on a pointwise multilayer-perceptron semantic segmentation network. The method consists of two main parts: electric power corridor semantic segmentation and individual power line extraction. In the segmentation step, SCF-Net is employed as our primary segmentation network, and the network can process large-scale point clouds. To further improve the segmentation ability of SCF-Net in the corridor, a local coding module is designed to construct the SCFL-Net. In the individual power line extraction step, individual power lines are extracted by flexible grid filtering, effectively overcoming the point-missing problem. The corridor point cloud semantic segmentation and individual power line extraction experiments are conducted in different corridors collected from suburban areas. Promising results are obtained for both semantic segmentation and individual power line extraction, with an mIou of point cloud semantic segmentation and a mean extraction rate of 96.70% and 96.56%, respectively.